A machine learning classifier using 12-lead ECG features detected pulmonary vein drivers of atrial fibrillation with 82.6% specificity and 73.9% sensitivity in a clinical dataset.
Does a machine learning algorithm using 12-lead ECG features accurately predict atrial fibrillation driver location and acute pulmonary vein ablation success?
A novel machine learning algorithm using 12-lead ECG features can noninvasively discriminate between PV and extra-PV drivers of AF, potentially identifying patients with high acute success rates for PVI.
BACKGROUND: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. OBJECTIVES: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. METHODS: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). RESULTS: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. CONCLUSION: Machine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.
Luongo et al. (Sat,) conducted a other in Atrial fibrillation (n=46). Machine learning-based classification of 12-lead ECG was evaluated on Detection of pulmonary vein drivers. A machine learning classifier using 12-lead ECG features detected pulmonary vein drivers of atrial fibrillation with 82.6% specificity and 73.9% sensitivity in a clinical dataset.